can-ai-be-used-to-refresh-messaging-more-efficiently-for-pharma-brands

1. Pharma brands can use a message refresh every 12
months, but often marketing teams don’t have new clinical
data or customer insights to refresh messaging.
Even when there is nothing new to say creatively,
messaging AI offers a faster, cheaper, better
alternative to refresh your messaging.
Can AI be used to refresh
messaging more efficiently
for pharma brands?

2. Several factors are leading to the need
for more frequent message refreshes:
Competitive Activity
Most disease states are significantly more competitive today than 10-20
years ago because multiple brands with equally good clinical data are
now-a-days launched within months of each other. The competitive activity is
intense with little first mover advantage and message wear-out happens
quickly in the market, even a brand’s messaging is highly effective.
Omni-channel Messaging Execution
COVID turbo charged the shift from personal promotion channel to
omni-channel execution for pharma messaging. A large % of physicians sold
their practices to IDNs during the pandemic and are now employees of large
health networks, which is changing their prescribing behaviors and also
changing how they interact with brand messaging.
More market events
Most brands are pursuing lifecycle management initiatives earlier in the
brand’s lifecycle, which is leading to more market events that require
messaging. If a competitor launches new Phase IV data, other brands in the
category may also have to refresh their messaging to piggyback on the class
effect or to defend share.
The future of pharma
marketing will require more
frequent message refreshes
The pharma commercial marketing model is
evolving rapidly and brand teams are discovering
that they need to refresh their HCP and patient
messaging more frequently to stay competitive.

3. Information Barriers
If the brand team feels that they don’t have enough “new information” to
drive a major message refresh, they are hesitant to even get started. New
information could constitute new clinical or real-world data, new customer
insights, new competitive intelligence, etc. Historically, since most message
refreshes were accompanied/driven by new clinical data, there can be a
prevailing belief among brand teams that, “If you have nothing new to say,
can you really say it differently enough?”
All pharma brands, big and small, are under intense pressure to maximize
profit even early in the lifecycle and funding a message refresh more
frequently can be challenging for brands. Management can question the
ROI of investing in more frequent message refreshes or require the brand
team to demonstrate an unrealistic return on the investment.
Budget Barriers
Pharma brand teams face many barriers to refresh their
messaging more frequently than the current message
refresh cycle of 18-24 months. As a result, many teams
don’t even undertake a message refresh when they
should or just make minor changes to their vis aid and
consider that a message refresh.
Barriers to refreshing
messaging more frequently

4. Process Barriers
The traditional processes established for a message refresh can be lengthy
and can take over 6 months to just develop and test messages in market
research.
Resource Barriers
Brand teams are often short on people and resources needed to manage a
message refresh and may have a limited contract with their agency of
record for messaging campaigns.
MLR approval is a major barrier to implementation for a message refresh.
Not only does it delay the process by weeks or months, it often results in
significant watering down of messages by the time they are actually
launched, questioning the logic of implementing a message refresh in the
first place.
Implementation Barriers

5. Predictive AI can be used to analyze
the effectiveness of marketing messages
for all brands in a disease state and identify
underperforming messages for your brand
without any customer feedback.
Generative AI can now be used to
generate messages for brands by
paraphrasing or even writing new content
based on prompts.
Finally, Evaluative AI can turbocharge
the way messages are tested with
customers in primary market research,
testing 100s of messages and billions of
storyflow options in one survey.
Messaging AI offers a different
approach to message refresh…one
that is faster, cheaper and better!
Artificial intelligence has made very significant progress in the past 5 years and
can be utilized as a supporting tool for many messaging related tasks.

6. Predictive AI
Use AI to analyze the effectiveness of
your messages vs. competitors and
benchmark databases.
Predictive AI is designed to make predictions for tasks
that would otherwise require customer feedback.
Historically, brand teams have used message recall,
brand ATUs and sales effectiveness type market
research studies to analyze the effectiveness of their
marketing messages. Predictive AI can learn from
past studies and make predictions on how effective
each message will be without needing new customer
feedback.
Since Predictive AI can score one message at a time,
it can be used to score messages at scale in a disease
state, i.e. 1,000s of messages from all competitors can
be analyzed for effectiveness quickly. Messages that
score low can be considered as underperforming
messages and can be the focus of the next message
refresh.
Using Predictive AI to analyze effectiveness of
messages can also allow industry database
comparisons, which can reveal gaps in messaging.
The analysis can be done quickly and at a low cost
compared to traditional methods like message recall,
ATU research, and sales effectiveness research.

7. Generative AI
Use AI to identify new ways of
articulating your clinical data
and your messages
Messages generated by AI can be further optimized
by applying decision heuristics science to them.
Decision heuristics science is the 3-time Nobel Prize
winning field of research that explains how humans
make decisions using mental shortcuts called
heuristics. Over the past 40 years, 600+ specific
decision heuristics have been discovered in academic
research, shedding light on the hidden drivers of
human decision making.
Physicians, patients and payers also use decision
heuristics to make decisions in a disease state.
Talking to their dominant decision heuristics through
fine-tuned language can make the branded and
unbranded messaging for pharma brands significantly
more compelling and persuasive.
With the introduction of large language models like
GPT-3, Jurassic, LAMDA, Bard, etc, generative AI has
made very significant progress and LLMs can now
generate marketing content like messages, taglines,
educational content within seconds. General purpose
LLMs like ChatGPT make a lot of errors in generating
content for highly technical and regulated industries
like pharma. However, large language models can be
fine tuned on pharma industry specific messaging
databases, and can be used to create branded and
unbranded messaging for pharma brands more
efficiently.

8. Evaluative AI
Use AI to test 100s of new
messages in one survey and find the
winning messaging story flow out of
billions of possibilities
Using artificial intelligence on data collected from
message testing surveys can produce optimal
message bundles and story flow out of billions of
possibilities and even personalize them down to the
segment and channel level. With evaluative AI,
pharma brands can get a channel- and
segment-specific messaging playbook that is ready to
execute instead of getting the conventional
deliverables of a message testing survey like a
message hierarchy, TURF analysis, etc.
AI can also be used live during a survey, learning from
the respondents’ choice patterns in real-time and
customizing future choices for each respondent in
order to get higher quality preference data from the
survey. When respondents are showed several highly
appealing choices, they are forced to think harder
about the choice, leading to more differentiated data.
Artificial intelligence can be used to make the output of
message testing surveys more actionable and
campaign ready. Historically, data from message
testing market research studies would be loaded into
statistical software systems like SPSS and the output
would be standard message hierarchies and/or a
TURF analysis.

9. Re-igniting growth for a
pharma brand with a
message refresh in just
Leveraging the power of AI to drive a
message refresh with 40+% improvement
in messaging performance even in the
absence of new clinical data or new
customer insights
12 weeks!
CASE STUDY

10. PRODUCT X is a longer-lasting injectable (LAI) form
introduced after patent expiration of a market leading oral.
While the LAI formulation is not as large in revenue as the
original formulation, it is still a blockbuster with over $1
billion in sales and several years of patent life left.
The LAI category competition was heating up with LCM
formulations of other older oral competitors as well as new
competitors entering the market. Product X had no new
clinical data and no recent market research on barriers to
the adoption of LCM dose, but the marketing team knew
that they needed an HCP message refresh.
The power of AI was used to lead a major message refresh
for the brand in less than 12 weeks, leading to a new
message story flow that had 40+% higher preference share
in market research testing.
Brand Situation

11. To analyze the effectiveness of PRODUCT X’s current messaging vs. competitors
and vs. a benchmark database, 700+ messages were collected from all brands in
the disease state. Branded and unbranded messages were collected from a
variety of channels including vis aid, website, in-office leave behind, physician
social media, etc.
Heuristics were appended to every message. Then, messages were scored on
effectiveness by predictive algorithms trained on data from past message testing
studies. Every message was predictively scored on a 3-tier grading system based
on how persuasive it will be to customers. All brands were compared in the
category against each other and against the Newristics database.
PRODUCT X current messaging had significant room for improvement vs.
benchmark database and some competitors. Since every message had been scored
individually, messages were broken up into groups based on performance and all
the underperforming messages were identified were targeted for improvement.
Step 1: Predictive AI
Effectiveness of PRODUCT X’s current messages
vs. competitors and benchmark database was
analyzed using Predictive AI
Database Product X Product A Product B Product C Product D
0%
10%
20%
30%
40%
50%
60%
70%
54%
41% 42%
53%
32%
56%
6% 5%
12%
38%
61%
1%
52%
30%
18%
9%
45%
46%
% of Messages with Tier 1 Rating
% of Messages with Tier 2 Rating
% of Messages with Tier 3 Rating

12. Even though no new clinical data was available to guide fine tuning and rewriting of
messages, decision heuristics science and generative AI was used to create many
alternative ways of articulating the same message through rephrasing. Since
generative AI is still in its early stages of development, pharma industry messaging
experts were used for human-in-the-loop review of all messages generated.
Alternative versions of each message in the current vis aid were generated based on
results of the heuristic analysis. All messages were organized into four groups and a
different message refinement strategy was used for each group.
Step 2: Generative AI
Large inventory of 400+ new messages for
Product X was generated with human-in-the-loop
AI trained on pharma heuristics
Example of Human-in-the-loop AI-based message refinement
Tier 1 Message
Tier 2 Message
Keep
Heuristic
Change
Heuristic
Group D
Messages that need
paraphrasing
Group C
Messages that need
significant rewrites
Group B
Messages that need
more editing
Group A
Messages that just
need fine-tuning
Current Vis Aid Message
Product X delivered rapid
response as early at T1 weeks,
and powerful efficacy at T2
months, even without Product Y.
Rewritten Message
Even without Y, Product X has the
power to deliver fast response at
T1 weeks, continuing efficacy at
T2 months.

13. AI-powered messaging testing made it possible to test >180 different
messages in one survey with only 237 HCPs. Respondents were matched
to PRODUCT X target list and also broken into behavioral segments based
on prescribing data.
With respondent-level data on 180+ messages, the algorithm first searched
among 209,556,357,120 possible message bundles and then identified the
optimal story flow for every page of the vis aid.
Data from the message testing was used to identify the
optimal story flow for every page of the new vis aid.
Step 3: Evaluative AI
180+ Product X messages were tested in
AI-powered quant research to identify best
message story flow for a new vis aid
Respondent level data
on 180+ messages
Algorithms search among
209,556,357,120
possible story flow options
Rules engine identified
optimal story flow for
every page of the vis aid
Vis Aid Pages

14. New messaging story flow performed 2.4x better vs.
current messaging in market research and led to an
immediate rollout of new vis aid.
Results
Product X message refresh
produced a story flow that was
at par with the market leader.
Product X message refresh
produced a story flow that was
at par with the market leader.
At par with
market leader
2.4X
improvement
Competitor 1
Market Leader
Product X
Message Refresh
Product X
Current Messaging
41% 40%
17%
Performance

15. New message refresh also identified
a major shift in messaging strategy
for the new vis aid.
Focus more on “risk-reduction” messages
The new vis aid storyflow put more emphasis on messages that communicate lower risk of
serious and/or negative events with Product X. Messages written to “risk reduction” related
endpoints became significantly more important in the new vis aid because they addressed
decision heuristics like Dread Risk Bias and Negativity Bias.
Reduction in risk of relapse
Reduction in risk of hospitalization
Reduction in recurrence of episodes
Focus less on “improvement” messages
Counterintuitively, messages focused on clinical endpoints related to the upside or improvement
in patient’s condition were not did not perform as well in the research and were de-emphasized
in the vis aid.
Improvement in Function
Improvement in Quality-of-Life
Evolve the brand positioning
The new messaging storyflow even laddered up to a potentially new brand positioning for
Product X based on the idea of a “safety net for patients who need protection.”
The brand team was already planning to take up a repositioning project after the message
refresh and the was able to accelerate the process since a powerful brand positioning
emerged organically from the research.
Differentiate Product X from competitors by
framing it as an easy choice
Product X allows for a more convenient transition from oral to LAI dosing, which makes it
easier to keep the patient treated with no gaps in care.
Product X is affordable for most patients, which means it’s easier for HCPs and their staff
to get their patients on Product X.
X